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Optical Reservoir Computing with Structural Nonlinearity for Forecasting Chaotic Time Series

Year: 2026

Type: preprint

Abstract: Optical computing offers a promising route to high-speed, energy-efficient information processing. However, implementing nonlinear operations typically requires strong light-matter interactions. Here, we introduce structural nonlinearity into an optical reservoir computing architecture, using only linear components. A digital micromirror device acts as a reconfigurable scattering potential, while the propagation of light through a scattering medium provides the high-dimensional mode mixing. By enforcing a second interaction with the wavefront shaper, we obtain a quadratic mapping of the input within the reservoir. A single experimental apparatus simultaneously implements conventional and structurally nonlinear reservoir architectures, enabling a direct comparison on forecasting of the chaotic Mackey-Glass time series. We demonstrate that the structurally nonlinear reservoir improves short-term prediction accuracy and better preserves the long-term dynamics of the chaotic system, demonstrating the potential of structural nonlinearity for low-power optical computing.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.02733

Authors: Venâncio, Leandro R., Dong, Jonathan, Mounaix, Mickael, Bertolotti, Jacopo

Institutions: The University of Queensland, University of Exeter, École Polytechnique Fédérale de Lausanne

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7207815427

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